Abstract

No abstract available.

Abstract

This paper presents new methods of estimating the bias and the resolution of radar and raingage area average rainfall measurements over a defined area when both devices are employed simultaneously.

The bias of raingage measurements for various rainfall amount ranges is estimated from published data, and the bias for radar measurement is then determined through comparison with the raingage recordings. The resolution estimations are carried out using error variance analysis on corresponding sets of gage and radar observations. The assumptions underlying this technique demand a uniform terrain for rainfall measurements, a large sample of cases, and, for one of the analysis options, a high correlation between radar and gage rainfall measurements.

The procedure is illustrated using the gage and radar rainfall data from the second phase of the Florida Area Cumulus Experiment (FACE-2). The gage sampling error variance estimations for various rainfall amount categories using an empirical radar-derived method are examined by comparison with those of published studies using alternate methods and are found to be in general agreement. The FACF,2 gage network is found to provide more highly resolved rainfall measurements than the WSR-57 radar in moderate or heavy rainfall, but the radar exhibits the superior resolution in light rainfall if the radar rainfall adjustment used in FACE is not carried out. The radar rainfall adjustment appears to reduce radar measurement bias quite effectively, but the resolution is generally not improved and is degraded in below-median rainfall amounts.

Abstract

In this study, the sources and strengths of statistical short-term climate predictability for local surface climate (temperature and precipitation) and 700-mb geopotential height in the Northern Hemisphere are explored at all times of the year at lead times of up to one year. Canonical correlation analysis is the linear statistical methodology employed. Predictor and predictand averaging periods of 1 and 3 months are used, with four consecutive predictor periods, followed by a lead time and then a single predictand period. Predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 700-mb height, and prior values of the predictand field itself. Cross-validation is used to obtain, to first order, uninflated skill estimates.

Results reveal mainly modest statistical predictive skill except for certain fields, locations, and times of the year when predictability is far above chance expectation and good enough to be beneficial to appropriate users. The time of year when skills are generally highest is January through April. Global SST is the most skill-producing predictor field, perhaps because 1) the lower boundary condition is a more consistent influence on climate on timescales of 1 to 3 months than the atmosphere's internal dynamics, or 2) SST is the only field in this study that provides tropical information directly. Prediction is generally more skillful on the 3-month than 1-month timesale. The skill of the forecasts is often insensitive to the forecast lead time; that is, inserting 3, or sometimes 6 or more, months between the predictor and predictand periods causes little skill decrease from that of 1 month or less. This has favorable implications for long-lead forecasting.

Much of the higher skill occurs in association with fluctuations of the El Niño/Southern Oscillation (ENSO) and is found in midwinter through midspring in specific pockets of the Pacific and North American regions. Predictive skill for precipitation is also found in the same context but is lower than that for 700-mb height or temperature.

Warm season predictability, slightly lower than that of winter-spring and not clearly documented in earlier work, is related to episodes of like-signed SST anomalies in the tropical oceans throughout the world in the preceding months. There is an interdecadal component in the variability of these global SST conditions. Generalized positive (negative) 700-mb and surface temperature anomalies in middle to late summer (but fall in southern Europe), generally at subtropical latitudes throughout much of the Northern Hemisphere (but with some midlatitude continental protrusions), occur following episodes of uniformly positive (negative) SST anomalies in the tropical oceans throughout the world in the preceding winter through late spring. The occurrence of a mature warm (cold) ENSO extreme the previous winter may contribute to such a worldwide SST condition in the intervening spring season. In the United States, the effect is a general (monopole) anomalous warmth (coolness) from mid-July through August across much of the country.

Abstract

The correspondence among the following three forecast verification scores, based on forecasts and their associated observations, is described: 1) the correlation score, 2) the root-mean-square error (RMSE) score, and 3) the Heidke score (based on categorical matches between forecasts and observations). These relationships are provided to facilitate comparisons among studies of forecast skill that use these differing measures.

The Heidke score would be more informative, more “honest,” and easier to interpret at face value if the severity of categorical errors (i.e., one-class errors versus two-class errors, etc.) were included in the scoring formula. Without taking categorical error severity into account the meaning of Heidke scores depends heavily on the categorical definitions (particularly the number of categories), making intercomparison between Heidke and correlation (or RMSE) scores, or even among Heidke scores, quite difficult.

When categorical error severity is taken into account in the Heidke score, its correspondence with other verification measures more closely approximates that of more sophisticated scoring systems such as the experimental LEPS score.

Abstract

Heat waves are climate extremes having significant environmental and social impacts. However, there is no universally accepted definition of a heat wave. The major goal of this study is to compare characteristics of continental U.S. warm season (May–September) heat waves defined using four different variables—temperature itself and three variables incorporating atmospheric moisture—all for differing intensity and duration requirements. To normalize across different locations and climates, daily intensity is defined using percentiles computed over the 1979–2013 period. The primary data source is the U.S. Historical Climatological Network (USHCN), with humidity data from the North American Regional Reanalysis (NARR) also tested and utilized. The results indicate that heat waves defined using daily maximum temperatures are more frequent and persistent than when based on minimum temperatures, with substantial regional variations in behavior. For all four temperature variables, heat waves based on daily minimum values have greater spatial coherency than for daily maximum values. Regionally, statistically significant upward trends (1979–2013) in heat wave frequency are identified, largest when based on daily minimum values, across variables. Other notable differences in behavior include a higher frequency of heat waves based on maximum temperature itself than for variables that include humidity, while daily minimum temperatures show greater similarity across all variables in this regard. Overall, the study provides a baseline to compare with results from climate model simulations and projections, for examining differing regional and large-scale circulation patterns associated with U.S. summer heat waves and for examining the role of land surface conditions in modulating regional variations in heat wave behavior.

Abstract

A global-scale decadal climate shift, beginning in 1998/99 and enduring through 2013, has been documented in recent studies, with associated precipitation shifts in key regions throughout the world. These precipitation shifts are most easily detected during March–May when ENSO effects are weak. Analyses have linked this climate shift to a shift in the Pacific decadal variability (PDV) pattern to its negative phase. Here the authors evaluate the predictive skill of the North American Multimodel Ensemble (NMME), and the CFSv2 model alone, in maintaining the observed precipitation shifts in seasonal forecasts, emphasizing the southwestern United States where deficient precipitation has tended to prevail since the late 1990s.

The NMME hindcasts out to 6 months lead are found to maintain the observed decadal precipitation shifts in key locations qualitatively correctly, but with increasingly underestimated amplitude with increasing lead time. This finding holds in the separate CFSv2 model hindcasts. The decadal precipitation shift is relatively well reproduced in the southwestern United States. The general underestimation of the precipitation shift is suggested to be related to a muted reproduction of the observed shift in Pacific sea surface temperature (SST). This conclusion is supported by runs from a different (but overlapping) set of atmospheric models, which when forced with observed SST reproduce the decadal shifts quite well. Overall, the capability of the NMME model hindcasts to reflect the observed decadal rainfall pattern shift, but with weakened amplitude (especially at longer leads), underscores the broader challenge of retaining decadal signals in predictions of droughts and pluvials at seasonal-to-interannual time scales.

Abstract

An empirical system for forecasting 3-month mean surface temperature T and total precipitation P for Canada—canonical correlation analysis (CCA)—has been developed using the 1956–90 data period. The levels and sources of predictive skill have been estimated for all seasons at lead times of up to one year, using a cross-validation design. The predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 500-mb geopotential height, and for T forecasts prior values of T itself. Four consecutive 3-month predictor periods are used to detect evolving as well as steady-state conditions in the predictor fields.

While forecast skills are modest for much of the year, winter and spring skills for T forecasts at a 3-month lead time are both highly statistically field significant and good enough to be beneficial to appropriate users. These forecasts average a 0.3–0.4 correlation skill nationwide and greater than 0.6 in the southeastern prairies. Forecast skill for P averages a lower but still statistically field significant 0.2 in winter with local maxima of greater than 0.5 along parts of southern Canada. A weak secondary seasonal maximum in T forecast skill is found in summer. CCA forecasts generally outperform persistence forecasts, and their skill declines only slowly as lead time is increased. Thus, useful forecasts can be made for certain seasons/regions of Canada several seasons in advance.

The CCA diagnostics indicate that the El Nin˜o/Southern Oscillation (ENSO) plays a dominant role in Canadian T anomalies in winter and spring, and P anomalies in winter. Warm SO (El Nin˜o) episodes tend to force positive winter and spring T anomalies in much of western and southern Canada, and suppressed P in roughly similar portions of the country. Below normal T tends to occur in northeastern Canada, and above normal P in the southeastern Northwest Territory, during warm SO episodes. Because of the linearity of CCA, opposite responses are implied for cold SO episodes. Another important skill source. for Canadian winter forecasts is associated with a long-term trend in global SST. Between the 1950s and the 1990s the high (low) latitude SST has tended to cool (warm). The Canadian winter T response has been a cooling from northern Quebec to northeastern Canada and warming in northwest Canada, while a trend toward greater (lighter) P in the northern (southern) prairies is noted. Knowledge of such trends can greatly aid in forecasting anomalies that are defined using normals for a period centered in the past.

In conclusion, statistically based long-lead forecasts of surface climate are shown to deliver useful skin in Canada. This approach also provides a skill benchmark against which the skill of dynamical models can be compared as they enter the forecasting arena.

Abstract

The extreme phases of El Niño–Southern Oscillation (ENSO) are known to dominate the interannual variability of tropical rainfall. However, the relationship between ENSO and the spatial extent of drought and excessively wet conditions is an important characteristic of the tropical climate that has received relatively less attention from researchers. Here, a standardized precipitation index is computed from monthly rainfall analyses and the temporal variability of the spatial extent of such extremes, for various levels of severity, is examined from a Tropics-wide perspective (land areas only, 30°S–30°N). Maxima in the spatial extent of both precipitation extremes are compared across multiple ENSO events that occurred during the period 1950–2003. The focus on tropical land areas is motivated by the numerous, often negative, impacts of ENSO-related precipitation variability on human populations.

Results show that major peaks in the spatial extent of drought and excessively wet conditions are generally associated with extreme phases of ENSO. A remarkably robust linear relationship is documented between the spatial extent of drought in the Tropics and El Niño strength (based on Niño-3.4 sea surface temperature anomalies), with a comparatively weaker relationship for La Niña and excessive wetness. Both conditions are found to increase by about a factor of 2 between strong and weak ENSO events, and in several locations they are shown to be more likely during ENSO events than at all other times, especially for severe categories. Relatively stronger El Niño events during recent decades are associated with increased drought extent in tropical land areas with increasing surface temperatures likely acting to exacerbate these dry conditions.

Abstract

A potentially operational system for 3-month total precipitation forecasts for island stations in the tropical Pacific has been developed at NOAA's Climate Prediction Center using the statistical method of canonical correlation analysis (CCA). Routine issuance of the forecasts could begin during 1996, presently they are issued experimentally. The levels and sources of predictive skills have been estimated at lead times of up to one year, using a cross-validation design. The predictor fields, in order of their predictive value, are quasi-global sea surface temperature, Northern Hemisphere 700-mb height, and prior values of the predictand precipitation itself. Four consecutive 3-nionth predictor periods are used to detect evolving as well as steady-state conditions.

Modest forecast skills are realized for most seasons of the year; however, moderate skills (correlation <0.5) are found for certain stations in the northern Tropics at lead times of 3 months or less in late northern winter, especially in the western Pacific. CCA generally outperforms persistence, even at short leads. The El Niñto-Southern Oscillation (ENSO) phenomenon is found to play the dominant role in the precipitation variability at many tropical Pacific islands. During especially the late northern winter of mature warm (cold) episodes, pre- cipitation is suppressed (enhanced) in a horwshoe-shaped region surrounding (to the north, west, south) the central and eastern equatorial zone. which is anomalously wet (dry).

A secondary source of predictive skill, most important for northern summer, is a pattern with like-signed SST anomalies over the Tropics of all three ocean basins. While this pattern may encompass ENSO episodes, it varies at lower frequencies than the ENSO phenomenon on its own.

Abstract

In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.